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Using Data to Shape the Global Learning Function


Using Data to Shape the Global Learning Function

Feature Story – By Jessica Knox

Virtually every industry is adopting new technology faster because of the pandemic; healthcare is no exception. Take, for example, the astounding growth of telehealth. The global telehealth market is set to almost triple (2.7x) between 2018 and 2024 in large part because of the pressures placed on healthcare systems due to COVID-19, according to Frontiers in Public Health magazine.

Precision and personalized medicine, considered a new era in healthcare, is heavily reliant on technologies ranging from advanced genetic profiling to the use of artificial intelligence (AI) in medical imaging.

An incredible opportunity that comes with digital is the increase in data. Data gives us the chance to use analytics, which, when done right, can enable better decision-making and ultimately lead to a greater impact, faster.

Building on the example of precision medicine, the more data we can collect about the genomic profiles of, for example, pancreatic cancer patients, paired with information about treatment decisions and outcomes, the more we can start to predict how specific patients will do on a particular course of treatment and make decisions that will support better outcomes.

The Global Learning Data Opportunity

The learning profession has been slower to use data to drive decision-making than many other industries. We’re still in our data infancy for a number of reasons, one being the messiness of human performance. Sure, we can collect the results of a multiple choice test but connecting training to meaningful outcomes is more difficult when you factor in all the other things that could impact results. We’re also missing an ability to standardize and share learning-related data across organizations that would provide a bigger data set and ultimately more robust analytics.

That messiness gets amplified for the global learning function. Global learning teams are removed from the local markets where implementation takes place, and because of that, some data-gathering and analysis activities can prove more challenging.

But global learning teams also have some unique data advantages. For starters, with global breadth comes larger data sets versus local markets, which can give you a greater confidence in the numbers. Furthermore, global learning teams often exist in part to drive efficiency of learning design and deployment at scale, which can lead to some unique measurement opportunities. Pair this with growing digital learning footprints due to the shift toward online program delivery, and the time is right to harness data for better global learning decision-making.

Below are some considerations for building data capability in the global learning function, compiled from years of working with global and local teams on learning analytics strategies.

Start Somewhere, Start Now

Let’s start with the concept of a data maturity model, in other words, a continuum that organizations can use to benchmark their data capabilities.

Reflecting on your organization’s and learning team’s data maturity will help you to appreciate that it’s a journey and set realistic goals for moving the needle. There are many data maturity models out there.

Not explicitly mentioned in some frameworks is the level of data-related skills required in the workforce as the organization matures. While most employees, the learning team included, do not need advanced data science skills by any means, they do need basic data analysis skills. Simply getting comfortable looking at and interpreting data takes time. Building meaningful use of data-driven insights into decision-making takes longer.

No matter where you are on the continuum, increasing the range of data sources  you look at will increase your comfort and confidence over time. It will flex your “data muscles” and help you get a sense of what data is available at your organization, how easy (or difficult) various data is to collect and how you might interpret the data if you were to use it in the future.

Table 1: Data to Collect & Reflect On

Table 1 has some examples of data you can start to collect and reflect on. At a global level, one of the big determining factors in data availability is the presence of global digital platforms such as human capital management (HCM) software, learning management systems (LMS), intranets and social networking platforms. You’ll have to work with the owners and administrators of each system to explore what data you can gather and to define a process for collection. Business intelligence teams can also be a big asset in understanding what’s available.

Find Metrics That Matter

Choosing clear, meaningful metrics is a critical step in formalizing your approach to measurement and analytics.

There are two approaches to defining metrics based on what you want to achieve:

  1. Focused on a specific program, for example onboarding.
  2. To measure overall global learning team performance with the goal of improving over time.

One useful framework as you’re defining metrics is adapted from Google’s approach to people analytics, where metrics belong in one of three categories:

  • Experience: perceived value and engagement from learners and other stakeholders.
  • Effectiveness: performance and results.
  • Efficiency: time and resources used to deliver outcomes.

Not all categories may be relevant to your goals, but the framework provides a good place to start. Some key questions to consider as you identify the right metrics include:

  • How will you use the answers? In other words, what decisions will you make based on what you learn?
  • Is the data already readily available? If not, how will you collect it?
  • How often do you have to collect the data and at what time?

Consider this global learning example, which is relatively simple but powerful. A global learning team has a core mandate to create product and therapeutic training to support several product launches across therapeutic areas at varying times and across a number of markets. For key markets, they provide translated materials while other markets would need to complete the translations themselves. Historically, the company had challenges ensuring the local markets were product training launch-ready at the right time.

Table 2: Four Key Metrics

They arrive at four key metrics to measure their impact and drive their decisions. Each of the following metrics can be sorted according to country, region, product and therapeutic area and also averaged for an overall pulse of how they’re doing. (See Table 2.) Answers to the questions tell them where they need to focus and conduct further analysis to determine the right course of action to improve their outcomes.

Keep Going

Beyond what’s been discussed here, there are other things to consider along your data journey. A clear ethics framework will help the team avoid potential privacy or other issues. How you communicate the insights and tell an effective story to stakeholders is a whole other skill that can be cultivated over time.

It takes time to build comfort and ability to use data effectively. Finding the right metrics is a big part of the work. It’s important to consider this an iterative process.  You may start with imperfect metrics or run into unanticipated data collection or quality problems. Over time, you’ll be able to hone your approach accordingly and propel the global learning function into the future.

Jessica Knox is CEO of Metrix Group. Email Jessica at

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